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  • 1.
    del Campo, Sergio Martin
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Albertsson, Kim
    Luleå University of Technology, Professional Support, IT-Service.
    Nilsson, Joakim
    Engineering Physics student at the Luleå University of Technology.
    Eliasson, Jens
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    FPGA prototype of machine learning analog-to-feature converter for event-based succinct representation of signals2013In: IEEE International Workshop on Machine Learning for Signal Processing, Piscataway, NJ: IEEE Signal Processing Society, 2013, article id 6661996Conference paper (Refereed)
    Abstract [en]

    Sparse signal models with learned dictionaries of morphological features provide efficient codes in a variety of applications. Such models can be useful to reduce sensor data rates and simplify the communication, processing and analysis of information, provided that the algorithm can be realized in an efficient way and that the signal allows for sparse coding. In this paper we outline an FPGA prototype of a general purpose "analog-to-feature converter", which learns an overcomplete dictionary of features from the input signal using matching pursuit and a form of Hebbian learning. The resulting code is sparse, event-based and suitable for analysis with parallel and neuromorphic processors. We present results of two case studies. The first case is a blind source separation problem where features are learned from an artificial signal with known features. We demonstrate that the learned features are qualitatively consistent with the true features. In the second case, features are learned from ball-bearing vibration data. We find that vibration signals from bearings with faults have characteristic features and codes, and that the event-based code enable a reduction of the data rate by at least one order of magnitude.

  • 2. Martin del Campo Barraza, Sergio
    Calibration and Testing of the Mercury Ion Analyzer (MIA) Sensor of the Mercury Plasma Particle Experiment (MPPE) onboard the BepiColombo Mission to Mercury2012Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Mercury is the closest planet to the Sun in the Solar System and because of this, its study has been a challenging task. BepiColombo/MMO is an orbiter part of a mission to Mercury with the goal of studying Mercury's magnetic field and magnetosphere. The orbiter is being developed by ISAS-JAXA. The studies are done using a five instruments payload. One of them is the Mercury Plasma Particle Experiment (MPPE) which will study low and high energy electrons/ions and energetic neutrals. The low-energy ion measurements are done using the Mercury Ion Analyzer (MIA) sensor, which is part of the MPPE.The MIA sensor requires calibration and testing to ensure its adequate operation. The testing will prove that the MIA sensor will be able to operate adequately in the Bepi-Colombo mission. This thesis work covers a series of factors that are required to verify the performance of the sensor. These factors are the degradation of the Micro-Channel Plate over time, the survival to vibration and thermal vacuum environments and the development of the software model of the sensor. These factors were evaluated with a MCP life test, qualification testing on the form of vibration and thermal vacuum tests and comparison of the sensor model response to the expected plasma environment around Mercury.The results of the MCP life test show that the MCP degrades faster at high temperatures, however, it will be able to survive the two year mission to Mercury. The qualification testing showed that the MIA sensor is able to withstand the vibration conditions in the mission. However, it will be until a new thermal vacuum test is done that it will be considered that the MIA sensor can withstand the expected thermal conditions. Finally, the response of the software model of the MIA sensor is in accordance to the expected plasma environment around Mercury. Therefore, it could be used to test the accuracy of the velocity moments calculation of the MDP1 software. Overall, the calibration and testing of the MIA sensor still continues. However, the results so far show that it will probe that it can operate under the conditions that the BepiColombo mission requires.

  • 3.
    Martin del Campo Barraza, Sergio
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Unsupervised feature learning for rotating machineryIn: International Journal of COMADEM, ISSN 1363-7681Article in journal (Other academic)
    Abstract [en]

    A smart sensorized bearing can be described as a bearing with built-in sensors for condition monitoring. These bearings require new methods for the processing of the information coming from the sensors. Smart bearings are expected to become the central components in the condition monitoring sensor system of future rotating machines. Condition monitoring typically requires expert knowledge about the machine that is monitored, making it costly to adapt the methods to the different machines, environment and operational conditions. This approach deals with an unsupervised learning method that allows for automatic characterization of signals with repeating structure in the time domain. The method is sparse coding with dictionary learning. We present the information obtained from time domain and frequency domain techniques and describe the conditions required to make the fault diagnosis possible. In contrast, we describe how our approach can autonomously depict deviations from the normal state of operation of machine by monitoring a dictionary of atomic waveforms learned from a signal. We study the propagation over time of a learned dictionary when the vibration of a rotating machine is monitored in normal and faulty states of operation, and we find that the adaptation rates of some atomic waveforms change significantly when a fault occurs.

  • 4.
    Martin del Campo Barraza, Sergio
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Schnabel, Stephan
    Kinematic Frequencies of Rotating Equipment Identified with Sparse Coding and Dictionary Learning2019In: Proceedings of the Annual Conference of the Prognostics and Health Management Society 2019 / [ed] N. Scott Clements, Scottsdale, AZ, USA, 2019, Vol. 11Conference paper (Refereed)
    Abstract [en]

    The detection of faults and operational abnormalities in rotating machine elements like rolling element bearings and gears requires information about kinematic properties, such as ball-pass and gear mesh frequencies. Typically, condition monitoring experts obtain such information from the manufacturers for diagnostics purposes. However, the reliability of such information can be compromised during installation and maintenance, for example, if components are replaced and do not match the documented specifications. Thus, methods enabling verification and online extraction of such kinematic properties are needed to improve diagnostic reliability. Unsupervised machine learning methods, like sparse coding with dictionary learning, enable automatic modeling and characterization of repeating signal structures in the time domain, which are naturally generated by rotating equipment. Sparse coding with dictionary learning represents a vibration signal as a linear superposition of noise and atomic waveforms. The activation rate of the atomic waveforms typically possesses a cyclic nature in rotating environments, similar to how bearing kinematic frequencies correlate with faults in a rolling element bearing. However, there is no explicit relationship between the activation rates of the atoms and the bearing kinematic frequencies. This motivates this investigation of the possibility to extract bearing kinematic frequencies from sparse representations. Former work describes the use of dictionary learning for the detection of anomalies in rolling element bearings. In this paper, we describe how a similar unsupervised machine learning method can be used to extract kinematic frequencies of bearings and gears, for example for anomaly detection purposes and comparisons with an expected signature. We study the activation rates and changes of atoms learned from vibration signals in two case studies. The first case is based on data from a well-known controlled experiment with faults seeded in the bearings. The second case is based on a public dataset recorded from the high-speed shaft of a wind turbine with a bearing failure. Furthermore, we compare the activation rates and weights of the atoms to the bearing kinematic frequencies and harmonics. Sparse coding with dictionary learning offers a possibility for self-learningof the kinematic frequencies of a bearing, which can be useful for the further improvement of automated anomaly detection methods in condition monitoring.

  • 5.
    Martin del Campo Barraza, Sergio
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Strömbergsson, Daniel
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.
    A dictionary learning approach to monitoring of wind turbine drivetrain bearings2017In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216Article in journal (Other academic)
    Abstract [en]

    Condition monitoring and predictive maintenance are central for efficient operation of wind farms due to the challenging operating conditions, rapid technology development and high number of aging wind turbines. In particular, preventive maintenance planning requires early detection of faults with few false positives. This is a challenging problem due to the complex and weak signatures of some faults, in particular of faults occurring in some of the drivetrain bearings. Here, we investigate recently proposed condition monitoring methods based on unsupervised dictionary learning using vibration data recorded from three wind turbines over about four years of operation, thereby contributing novel test results based on real world data. Results of former studies addressing condition--monitoring tasks using dictionary learning indicate that unsupervised feature learning is useful for diagnosis and anomaly detection purposes. However, these studies are based on data from test rigs operating under controlled conditions. Furthermore, most former studies focus on classification tasks using relatively small sets of labeled data, which are useful for quantitative method comparisons but gives little information about how useful these approaches are in practice. In this study dictionaries are learned from gearbox vibrations in three different turbines known to be in healthy conditions, and the dictionaries are subsequently propagated over a few years of monitoring data when faults are known to occur. We calculate the dictionary distance between the initial and propagated dictionaries and find time periods of abnormal dictionary adaptation starting six months before a drivetrain bearing replacement and one year before the resulting gearbox replacement. When repeating that experiment with a dictionary that initially is learned from the vibration of another type of rotating machine, the corresponding difference of dictionary distances is three times lower and do not appear abnormal. We also investigate the distance between dictionaries learned from geographically nearby turbines of the same type in healthy conditions and find that the features learned are similar, and that a dictionary learned from one turbine can be useful for monitoring of another similar turbine.

  • 6.
    Martin del Campo Barraza, Sergio
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Strömbergsson, Daniel
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.
    Dataset concerning the vibration signals from wind turbines in northern Sweden2018Data set
    Abstract [en]

    In the manuscript, we investigate condition monitoring methods based on unsupervised dictionary learning.

    The dataset includes the raw time-domain vibration signals from six turbines within the same wind farm (near geographical location). All the wind turbines are of the same type and possess a three-stage gearbox. All measurement data corresponds to the axial direction of an accelerometer mounted on the housing of the output shaft bearing of each turbine. The sampling rate is 12.8 kilosamples/second and each signal segment is 1.28 seconds long (16384 samples).

  • 7.
    Martin del Campo Barraza, Sergio
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Schnabel, Stephan
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Marklund, Pär
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.
    Detection of particle contaminants in rolling element bearings with unsupervised acoustic emission feature learning2019In: Tribology International, ISSN 0301-679X, E-ISSN 1879-2464, Vol. 132, p. 30-38Article in journal (Refereed)
    Abstract [en]

    The detection of contaminants in the lubricant of rolling element bearings using acoustic emission signals is a challenging problem, in particular at high rotational speeds. This problem calls for new analysis methods beyond the conventional amplitude- and frequency-based methods. Feature learning is successfully used in the machine learning field to characterize complex signals. Here we use an unsupervised feature learning approach to distinguish acoustic emission signals. We investigate the repetition rates of features identified with shift-invariant dictionary learning and find that the signature of contaminated lubricant is significantly stronger than the effect on conventional condition indicators like the RMS and the enveloped RMS at rotational speeds above 300 rpm and up to 3000 rpm.

  • 8.
    Martin-del-Campo, Sergio
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Online feature learning for condition monitoring of rotating machinery2017In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 64, p. 187-196Article in journal (Refereed)
    Abstract [en]

    Condition-based maintenance of rotating machinery requires efficient condition monitoring methods that enable early detection of abnormal operational conditions and faults. This is a challenging problem because machines are different and change characteristics over time due to wear and maintenance. The efficiency and scalability of conventional condition monitoring methods are limited by the need for manual analysis and re-configuration. The problem to extract relevant features from condition monitoring signals and thereby detect and analyze changes in such signals is a central issue, which in principle can be addressed using machine learning methods. Former work demonstrates that dictionary learning can be used to automatically derive signal features that characterize different operational conditions and faults of a rotating machine, but the use of such methods for online condition monitoring purposes is an open problem. Here we investigate online learning of features using dictionary learning. We describe dictionary distance and signal fidelity based heuristics for anomaly detection, and we study the time--propagated features and sparse approximation of vibration and acoustic emission signals in three different case studies. We present results of numerical experiments with different hyperparameters affecting the approximation accuracy, computational cost, and the adaptation rate of the learned features. We find that the learned features change rapidly when a fault appears in the machine or changes characteristics, and that the dictionary is different in normal and faulty conditions. We find that the learned features change slowly under normal variations of the operational conditions in comparison to the rapid adaptation observed when a fault appears (bearing defects, magnetite particles in the lubricant, or plastic deformation of steel). Furthermore, a sparse signal approximation with 2.5\% preserved coefficients based on a propagated dictionary is sufficient for anomaly detection in the cases considered here. Furthermore, we find that a sparse signal approximation with 2.5\% preserved coefficients based on a propagated dictionary is sufficient for bearing defect detection.

  • 9.
    Martin-del-Campo, Sergio
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sparse feature learning for condition monitoring2014Conference paper (Other academic)
  • 10.
    Martin-del-Campo, Sergio
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Towards zero-configuration condition monitoring based on dictionary learning2015In: Proceedings of the 23rd European Signal Processing Conference (EUSIPCO 2015): Aug. 31 2015-Sept. 4 2015, Nice, Piscatataway, NJ: IEEE Communications Society, 2015, p. 1306-1310, article id 7362595Conference paper (Refereed)
    Abstract [en]

    Condition-based predictive maintenance can significantly improve overall equipment effectiveness provided that appropriate monitoring methods are used. Online condition monitoring systems are customized to each type of machine and need to be reconfigured when conditions change, which is costly and requires expert knowledge. Basic feature extraction methods limited to signal distribution functions and spectra are commonly used, making it difficult to automatically analyze and compare machine conditions. In this paper, we investigate the possibility to automate the condition monitoring process by continuously learning a dictionary of optimized shift-invariant feature vectors using a well-known sparse approximation method. We study how the feature vectors learned from a vibration signal evolve over time when a fault develops within a ball bearing of a rotating machine. We quantify the adaptation rate of learned features and find that this quantity changes significantly in the transitions between normal and faulty states of operation of the ball bearing.

  • 11.
    Martin-del-Campo, Sergio
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Schnabel, Stephan
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.
    Marklund, Pär
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Exploratory Analysis of Acoustic Emissions in Steel using Dictionary Learning2016In: IEEE Ultrasonics Symposium 2016, Tours France, September 18-21, 2016, Piscataway, NJ: IEEE conference proceedings, 2016, article id 7728825Conference paper (Refereed)
    Abstract [en]

    Analysis of acoustic emissions (AE) from steel deformation is a challenging condition monitoring problem due to the high frequencies and data rates involved, and the difficulty to separate signals from noise. The problem to characterize and identify different AE sources calls for methods that goes beyond conventional time and frequency domain analysis. Feature learning is common in the field of machine learning and is successfully used to approximate and classify other kinds of complex signals. Former studies show that AE classification results depend on the choice of predefined features that are extracted from the raw AE signal, but little is known about feature learning in this context. Here we use dictionary learning and sparse coding to optimize a set of shift-invariant features to the AE signal measured in a steel tensile strength test. The specimen undergoes elastic and plastic deformation and eventually cracks. We investigate the learned features and their repetition rates and use principal component analysis (PCA) to illustrate that the resulting sparse AE code is useful for classification of the three strain stages, without reference to the signal amplitude. Therefore, feature learning is a potentially useful approach to the AE analysis problem, which also opens up for further studies of automated methods for anomaly detection in AE.

  • 12.
    Martín del Campo Barraza, Sergio
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Towards autonomous condition monitoring sensor systems2015Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Rolling element bearings are used to carry load and reduce friction between moving parts in rotating machines, which play a central role in society and industry, for example in the transportation and energy sectors. It is essential to monitor and maintain the condition of bearings such that machines can operate efficiently and any failures resulting in unplanned stoppages are avoided. Therefore, bearings with embedded sensing capabilities are becoming increasingly common, which makes it possible to consider bearings to be sensor systems that can monitor the condition of rotating machines. However, the task of automatically analyzing the signals is challenging because machines are different and evolve over time; moreover, the complexity of the signals, machines and possible failure modes is high and costly to accurately predict and model. Therefore, the use of unsupervised machine learning methods for the automated analysis of such signals and the detection of abnormal operational conditions is an interesting subject worth further exploration.Previous work has strongly depended on static features defined by human experts and thresholds that characterize abnormal operational conditions. Furthermore, machine learning methods typically depend on such static features to classify the faults and various operational conditions of the machine. This approach is challenging when reusing a method for different applications and environments, wherein similar features and thresholds can have different meanings. This problem is typically solved by reconfiguring or redesigning the condition monitoring system, thereby constraining the applicability and efficiency of the method.In this licentiate thesis, I investigate unsupervised methods for feature learning and anomaly detection. In particular, I focus on vibration signals, which contain information about both the bearing condition and the condition of the machine.The considered model represents the signal as a linear superposition of noise and atomic waveforms of arbitrary shape, amplitude and position. The atomic waveforms are adapted to each signal and machine using an unsupervised probabilistic optimization method and are considered features of the machine and physical processes exciting the signal. This model can automatically adapt the features to different environmental and operational conditions, thereby forming the basis for the development of a condition monitoring system that requires a minimum of manual configuration. Additionally, the model produces sparse codes that decrease the sensor data rate and, in principle, simplify the task of analyzing and communicating complex sensor information in resource-constrained embedded sensor systems.The thesis outlines an implementation of a sparse representation and dictionary learning method that is applied to vibration signals. I describe how signal analysis is performed using typical static pre-defined features and contrast this analysis with an analysis based on features that are automatically derived from the signal. In particular, the analysis focuses on the evolution of the vibration signal and the features when a fault develops within the ball bearing of a rotating machine. The evolution rate of learned features is defined and proposed as an interesting quantity for an autonomous condition monitoring process, and a first step towards an FPGA implementation of the method is presented.

  • 13.
    Martín del Campo Barraza, Sergio
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Unsupervised feature learning applied to condition monitoring2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Improving the reliability and efficiency of rotating machinery are central problems in many application domains, such as energy production and transportation. This requires efficient condition monitoring methods, including analytics needed to predict and detect faults and manage the high volume and velocity of data. Rolling element bearings are essential components of rotating machines, which are particularly important to monitor due to the high requirements on the operational conditions. Bearings are also located near the rotating parts of the machines and thereby the signal sources that characterize faults and abnormal operational conditions. Thus, bearings with embedded sensing, analysis and communication capabilities are developed.

     

    However, the analysis of signals from bearings and the surrounding components is a challenging problem due to the high variability and complexity of the systems. For example, machines evolve over time due to wear and maintenance, and the operational conditions typically also vary over time. Furthermore, the variety of fault signatures and failure mechanisms makes it difficult to derive generally useful and accurate models, which enable early detection of faults at reasonable cost. Therefore, investigations of machine learning methods that avoid some of these difficulties by automated on-line adaptation of the signal model are motivated. In particular, can unsupervised feature learning methods be used to automatically derive useful information about the state and operational conditions of a rotating machine? What additional methods are needed to recognize normal operational conditions and detect abnormal conditions, for example in terms of learned features or changes of model parameters?

     

    Condition monitoring systems are typically based on condition indicators that are pre-defined by experts, such as the amplitudes in certain frequency bands of a vibration signal, or the temperature of a bearing. Condition indicators are used to define alarms in terms of thresholds; when the indicator is above (or below) the threshold, an alarm indicating a fault condition is generated, without further information about the root cause of the fault. Similarly, machine learning methods and labeled datasets are used to train classifiers that can be used for the detection of faults. The accuracy and reliability of such condition monitoring methods depends on the type of condition indicators used and the data considered when determining the model parameters. Hence, this approach can be challenging to apply in the field where machines and sensor systems are different and change over time, and parameters have different meaning depending on the conditions. Adaptation of the model parameters to each condition monitoring application and operational condition is also difficult due to the need for labeled training data representing all relevant conditions, and the high cost of manual configuration. Therefore, neither of these solutions is viable in general.

     

    In this thesis I investigate unsupervised methods for feature learning and anomaly detection, which can operate online without pre-training with labeled datasets. Concepts and methods for validation of normal operational conditions and detection of abnormal operational conditions based on automatically learned features are proposed and studied. In particular, dictionary learning is applied to vibration and acoustic emission signals obtained from laboratory experiments and condition monitoring systems. The methodology is based on the assumption that signals can be described as a linear superposition of noise and learned atomic waveforms of arbitrary shape, amplitude and position. Greedy sparse coding algorithms and probabilistic gradient methods are used to learn dictionaries of atomic waveforms enabling sparse representation of the vibration and acoustic emission signals. As a result, the model can adapt automatically to different machine configurations, and environmental and operational conditions with a minimum of initial configuration. In addition, sparse coding results in reduced data rates that can simplify the processing and communication of information in resource-constrained systems.

     

    Measures that can be used to detect anomalies in a rotating machine are introduced and studied, like the dictionary distance between an online propagated dictionary and a set of dictionaries learned when the machine is known to operate in healthy conditions. In addition, the possibility to generalize a dictionary learned from the vibration signal in one machine to another similar machine is studied in the case of wind turbines.

     

    The main contributions of this thesis are the extension of unsupervised dictionary learning to condition monitoring for anomaly detection purposes, and the related case studies demonstrating that the learned features can be used to obtain information about the condition. The cases studies include vibration signals from controlled ball bearing experiments and wind turbines; and acoustic emission signals from controlled tensile strength tests and bearing contamination experiments. It is found that the dictionary distance between an online propagated dictionary and a baseline dictionary trained in healthy conditions can increase up to three times when a fault appears, without reference to kinematic information like defect frequencies. Furthermore, it is found that in the presence of a bearing defect, impulse-like waveforms with center frequencies that are about two times higher than in the healthy condition are learned. In the case of acoustic emission analysis, it is shown that the representations of signals of different strain stages of stainless steel appear as distinct clusters. Furthermore, the repetition rates of learned acoustic emission waveforms are found to be markedly different for a bearing with and without particles in the lubricant, especially at high rotational speed above 1000 rpm, where particle contaminants are difficult to detect using conventional methods. Different hyperparameters are investigated and it is found that the model is useful for anomaly detection with as little as 2.5 % preserved coefficients.

  • 14.
    Ramstad, Robin
    et al.
    Swedish Institute of Space Physics / Institutet för rymdfysik.
    Futaana, Yoshifumi
    Swedish Institute of Space Physics / Institutet för rymdfysik.
    Barabash, Stas
    Swedish Institute of Space Physics / Institutet för rymdfysik.
    Nilsson, Hans
    Swedish Institute of Space Physics / Institutet för rymdfysik.
    Martin del Campo B, Sergio
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Lundin, Rickard
    Swedish Institute of Space Physics / Institutet för rymdfysik.
    Schwingenschuh, Konrad
    Space Research Institute, Austrian Academy of Sciences.
    Phobos 2/ASPERA data revisited: Planetary ion escape rate from Mars near the 1989 solar maximum2013In: Geophysical Research Letters, ISSN 0094-8276, E-ISSN 1944-8007, Vol. 40, no 3, p. 477-481Article in journal (Refereed)
    Abstract [en]

    1] Insights about the near-Mars space environment from Mars Express observations have motivated a revisit of the Phobos 2/ASPERA ion data from 1989. We have expanded the analysis to now include all usable heavy ion (O+, O, CO) measurements from the circular orbits of Phobos 2. Phobos 2/ASPERA ion fluxes in the Martian tail are compared with previous results obtained by the instruments on Phobos 2. Further validation of the measurement results is obtained by comparing IMP-8 and Phobos 2/ASPERA solar wind ion fluxes, taking into account the time lag between Earth and Mars. Heavy ion flux measurements from 18 circular equatorial orbits around Mars are bin-averaged to a grid, using the MSE (electric field) frame of reference. The binned data are subsequently integrated to determine the total escape rate of planetary ions. From this we derive a total planetary heavy ion escape rate of (2–3) × 1025 s−1 from Mars for the 1989 solar maximum

  • 15.
    Sandin, Fredrik
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Martin-del-Campo, Sergio
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Dictionary Learning with Equiprobable Matching Pursuit2017Conference paper (Refereed)
    Abstract [en]

    Sparse signal representations based on linear combinations of learned atomshave been used to obtain state-of-the-art results in several practical signalprocessing applications. Approximation methods are needed to processhigh-dimensional signals in this way because the problem to calculate optimalatoms for sparse coding is NP-hard. Here we study greedy algorithms forunsupervised learning of dictionaries of shift-invariant atoms and propose anew method where each atom is selected with the same probability on average,which corresponds to the homeostatic regulation of a recurrent convolutionalneural network. Equiprobable selection can be used with several greedyalgorithms for dictionary learning to ensure that all atoms adapt duringtraining and that no particular atom is more likely to take part in the linearcombination on average. We demonstrate via simulation experiments thatdictionary learning with equiprobable selection results in higher entropy ofthe sparse representation and lower reconstruction and denoising errors, bothin the case of ordinary matching pursuit and orthogonal matching pursuit withshift-invariant dictionaries. Furthermore, we show that the computational costsof the matching pursuits are lower with equiprobable selection, leading tofaster and more accurate dictionary learning algorithms.

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